This setup is aggregated to the three configurations of our predictive model, FL, CML, and LML. As both main gated RNN architectures, LSTM [1]}, and GRU [2]} have shown to perform equally well on tasks comparable to ours [3]}, [4]}, we focus on the less resource-intensive GRU in this study.
Our basic DL model architecture (similar to [3]} and [4]}) consists of two parallel input layers, one for the vital signs and one for the laboratory variables, each followed by three recurrent layers of 16 GRUs. Consecutively, we perform batch normalization and combine the resulting outputs using two fully connected layers. Finally, we add a sigmoid-layer for estimating the patient risk of ICU mortality with a value between 0 and 1. Matching to this binary output of the system, we use binary cross-entropy as the loss function \(L(\cdot )\) .
